394 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" uni jobs in Sweden
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- University of Gothenburg, Department of Education, Communication and Learning
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conducting research "in the wild" (e.g., field deployments or data collection in real-world environments) Familiarity with current AI technologies (e.g., machine learning, large language models) and an
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publication list. Where to apply Website https://lu.varbi.com/en/what:job/jobID:907034/type:job/where:39/apply:1 Requirements Research FieldMedical sciencesEducation LevelPhD or equivalent Research FieldMedical
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lecturer who can complement our team with expertise in gender studies that is clearly oriented towards global studies and/or human rights, and who can teach, supervise and communicate with students in
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at Lake Kvismaren, Närke, involving daily observations of all breeding GRW, collect blood samples and data of breeding performance. The project gives excellent opportunity to learn DNA techniques
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of advancing Swedish academia and industry to the forefront of quantum technology, and to build a Swedish quantum computer. The student contributes to this project to explore fundamental and applied questions in
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-performance computing. SLU provides access to extensive datasets that can be used to develop machine learning methods and automated analyses relevant to the position. Long-term datasets are available from, i.a
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their working language within four years of beginning the position. The department will provide support with language learning. Eligibility The applicant must: be licensed to practise veterinary medicine
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, workplace learning, educational leadership, communication, school development, didactics and teaching. For more information about doctoral education in education at the University of Gothenburg: https
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Master's degree in computer science, computer engineering, or equivalent. Demonstrate proficiency in English (reading, writing, speaking). Show the ability to work independently and in a team, as
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, or quantum-inspired methods Experience with hybrid quantum–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience